Project
There is a need for methods that can automate the analysis of data from underwater imaging sensors used for health monitoring of our oceans. This includes object detection, tracking, and analysis of marine organisms in various habitats. Currently, there exists only a fraction of public available datasets of underwater scenes and the amount of research within the marine vision field is limited. The goal of this project is to develop methods for automated behavioral analysis of marine organisms in the wild. The road towards this goal involves development of methods for automated behavioral analysis of fish in controlled environments, anomaly detection in the wild, and classification and tracking of marine organisms in the wild.
PhD Thesis
Exploring New Waters: Advancing Fish Monitoring with Computer Vision
Pedersen, M., 2023, Aalborg Universitetsforlag.
Scientific Work
Finding Nemo’s Giant Cousin: Keypoint Matching for Robust Re-Identification of Giant Sunfish
Pedersen, M., Nyegaard, M. & Moeslund, T. B., maj 2023, I: Journal of Marine Science and Engineering. 11, 5, 889.
BrackishMOT: The Brackish Multi-Object Tracking Dataset
Pedersen, M., Lehotský, D., Nikolov, I. A. & Moeslund, T. B., apr. 2023, Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I.. Gade, R., Felsberg, M. & Kämäräinen, J-K. (red.). Springer, s. 17-33 17 s. (Lecture Notes in Computer Science, Bind LNCS 13885).
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric
Pedersen, M., Haurum, J. B., Dendorfer, P. & Moeslund, T. B., jul. 2022, (Accepteret/In press) Computer Vision – ECCV 2022. (Lecture Notes in Computer Science (LNCS)).
– Link to project-page: MOTCOM
Re-Identification of Giant Sunfish using Keypoint Matching
Pedersen, M., Haurum, J. B., Moeslund, T. B. & Nyegaard, M., 28 mar. 2022, I: Eludamos. Vol. 3 (2022): Proceedings of the Northern Lights Deep Learning Workshop 2022
3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset
Pedersen, M., Haurum, J. B., Bengtson, S. H. & Moeslund, T. B., 5 aug. 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): IEEE, s. 2426-2436 11 s. (I E E E Conference on Computer Vision and Pattern Recognition. Proceedings).
– Link to project-page: 3D-ZeF
Detection of Marine Animals in a New Underwater Dataset with Varying Visibility
Pedersen, M., Haurum, J. B., Gade, R., Moeslund, T. B. & Madsen, N., jun. 2019, IEEE Conference on Computer Vision and Pattern Recognition Workshops.IEEE, 10 s. (IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)).
– Link to project-page: The Brackish Dataset
Camera Calibration for Underwater 3D Reconstruction Based on Ray Tracing using Snell’s Law
Pedersen, M., Bengtson, S. H., Gade, R., Madsen, N. & Moeslund, T. B., 13 dec. 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).IEEE, s. 1410-1417 8 s. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).
Essays
No Machine Learning Without Data: Critical Factors to Consider when Collecting Video Data in Marine Environments
Pedersen, M., Madsen, N. & Moeslund, T. B., okt. 2021, I: The Journal of Ocean Technology. 16, 3, s. 21-30 10 s.
Fishing with C-TUCs (Cheap Tiny Underwater Cameras) in a sea of possibilities
Madsen, N., Pedersen, M., Jensen, K. T., Møller, P. R., Andersen, R. E. & Moeslund, T. B., 2021, I: The Journal of Ocean Technology. 16, 2, s. 19-30 12 s.
Funding
Marine Analytics using Computer Vision is funded by Danmarks Frie Forskningsfond under the case number: 9131-00128B
Contact
PhD-Student: Malte Pedersen
Email: mape@create.aau.dk
Supervisor: Thomas B. Moeslund
Email: tbm@create.aau.dk
